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Parallel conjugate gradient [electronic resource] : effects of ordering strategies, programming paradigms, and architectural platforms

Published
Washington, D.C. : United States. Department of Energy. Office of Advanced Scientific Computing Research, 2000.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy.
Physical Description
15 pages : digital, PDF file
Additional Creators
Lawrence Berkeley National Laboratory, United States. Department of Energy. Office of Advanced Scientific Computing Research, United States. National Aeronautics and Space Administration, and United States. Department of Energy. Office of Scientific and Technical Information
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Summary
The Conjugate Gradient (CG) algorithm is perhaps the best-known iterative technique to solve sparse linear systems that are symmetric and positive definite. A sparse matrix-vector multiply (SPMV) usually accounts for most of the floating-point operations with a CG iteration. In this paper, we investigate the effects of various ordering and partitioning strategies on the performance of parallel CG and SPMV using different programming and architectures. Results show that for this class of applications, ordering significantly improves overall performance, that cache reuse may be more important than reducing communication, and that it is possible to achieve message passing performance using shared memory constructs through careful data ordering and distribution. However, a multithreaded implementation of CG on the Tera MTA does not require special ordering or partitioning to obtain high efficiency and scalability.
Report Numbers
E 1.99:lbnl--45828
lbnl--45828
Subject(s)
Other Subject(s)
Note
Published through SciTech Connect.
05/01/2000.
"lbnl--45828"
13th International Conference on Parallel and Distributed Computing Systems, Las Vegas, NV (US), 08/08/2000--08/10/2000.
Li, X.; Biswas, R.; Heber, G.; Oliker, L.
Funding Information
AC03-76SF00098
618310
View MARC record | catkey: 14350573